
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile

from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version


UpperCAmelCase       =					get_logger(__name__)







class 			A_  :




	'''simple docstring'''


	_UpperCamelCase	:	Dict									=			"""dummy_data"""
	_UpperCamelCase	:	Optional[int]									=			"""datasets"""
	_UpperCamelCase	:	Tuple									=			False





	def __init__(			self						,       snake_case						,       snake_case						,       snake_case						,       snake_case = None						,       snake_case = False						,       snake_case = True						,       snake_case = None						,       ):
		lowercase	    =       0
		lowercase	    =       dataset_name
		lowercase	    =       cache_dir
		lowercase	    =       use_local_dummy_data
		lowercase	    =       config
		# download_callbacks take a single url as input
		lowercase	    =       download_callbacks or []
		# if False, it doesn't load existing files and it returns the paths of the dummy files relative
		# to the dummy_data zip file root
		lowercase	    =       load_existing_dummy_data

		# TODO(PVP, QL) might need to make this more general
		lowercase	    =       str(snake_case			)
		# to be downloaded
		lowercase	    =       None
		lowercase	    =       None





	@property
	def       SCREAMING_SNAKE_CASE__		(			self			):
		if self._dummy_file is None:
			lowercase	    =       self.download_dummy_data()
		return self._dummy_file





	@property
	def       SCREAMING_SNAKE_CASE__		(			self			):
		if self.config is not None:
			# structure is dummy / config_name / version_name
			return os.path.join('dummy'						,       self.config.name						,       self.version_name			)
		# structure is dummy / version_name
		return os.path.join('dummy'						,       self.version_name			)





	@property
	def       SCREAMING_SNAKE_CASE__		(			self			):
		return os.path.join(self.dummy_data_folder						,       'dummy_data.zip'			)





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       (
		    self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
		)

		lowercase	    =       cached_path(
		    snake_case						,       cache_dir=self.cache_dir						,       extract_compressed_file=snake_case						,       force_extract=snake_case			)

		return os.path.join(snake_case						,       self.dummy_file_name			)





	@property
	def       SCREAMING_SNAKE_CASE__		(			self			):
		return os.path.join(self.datasets_scripts_dir						,       self.dataset_name						,       self.dummy_zip_file			)





	@property
	def       SCREAMING_SNAKE_CASE__		(			self			):
		if self._bucket_url is None:
			lowercase	    =       hf_github_url(self.dataset_name						,       self.dummy_zip_file.replace(os.sep						,       '/'			)			)
		return self._bucket_url





	@property
	def       SCREAMING_SNAKE_CASE__		(			self			):
		# return full path if its a dir
		if os.path.isdir(self.dummy_file			):
			return self.dummy_file
		# else cut off path to file -> example `xsum`.
		return "/".join(self.dummy_file.replace(os.sep						,       '/'			).split('/'			)[:-1]			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       *snake_case			):
		if self.load_existing_dummy_data:
			# dummy data is downloaded and tested
			lowercase	    =       self.dummy_file
		else:
			# dummy data cannot be downloaded and only the path to dummy file is returned
			lowercase	    =       self.dummy_file_name

		# special case when data_url is a dict
		if isinstance(snake_case						,       snake_case			):
			return self.create_dummy_data_dict(snake_case						,       snake_case			)
		elif isinstance(snake_case						,       (list, tuple)			):
			return self.create_dummy_data_list(snake_case						,       snake_case			)
		else:
			return self.create_dummy_data_single(snake_case						,       snake_case			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       *snake_case			):
		return self.download_and_extract(snake_case			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case			):
		return self.download_and_extract(snake_case			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       *snake_case						,       **snake_case			):
		return path





	def       SCREAMING_SNAKE_CASE__		(			self			):
		return {}





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case			):
		lowercase	    =       {}
		for key, single_urls in data_url.items():
			for download_callback in self.download_callbacks:
				if isinstance(snake_case						,       snake_case			):
					for single_url in single_urls:
						download_callback(snake_case			)
				else:
					lowercase	    =       single_urls
					download_callback(snake_case			)
            # we force the name of each key to be the last file / folder name of the url path
            # if the url has arguments, we need to encode them with urllib.parse.quote_plus
			if isinstance(snake_case						,       snake_case			):
				lowercase	    =       [os.path.join(snake_case						,       urllib.parse.quote_plus(Path(snake_case			).name			)			) for x in single_urls]
			else:
				lowercase	    =       single_urls
				lowercase	    =       os.path.join(snake_case						,       urllib.parse.quote_plus(Path(snake_case			).name			)			)
			lowercase	    =       value

		# make sure that values are unique
		if all(isinstance(snake_case						,       snake_case			) for i in dummy_data_dict.values()			) and len(set(dummy_data_dict.values()			)			) < len(
		    dummy_data_dict.values()			):
			# append key to value to make its name unique
			lowercase	    =       {key: value + key for key, value in dummy_data_dict.items()}

		return dummy_data_dict





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case			):
		lowercase	    =       []
		# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
		lowercase	    =       all(bool(re.findall('[0-9]{3,}-of-[0-9]{3,}'						,       snake_case			)			) for url in data_url			)
		lowercase	    =       all(
		    url.startswith('https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed'			) for url in data_url			)
		if data_url and (is_tf_records or is_pubmed_records):
			lowercase	    =       [data_url[0]] * len(snake_case			)
		for single_url in data_url:
			for download_callback in self.download_callbacks:
				download_callback(snake_case			)
			# we force the name of each key to be the last file / folder name of the url path
			# if the url has arguments, we need to encode them with urllib.parse.quote_plus
			lowercase	    =       os.path.join(snake_case						,       urllib.parse.quote_plus(single_url.split('/'			)[-1]			)			)
			dummy_data_list.append(snake_case			)
		return dummy_data_list





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case			):
		for download_callback in self.download_callbacks:
			download_callback(snake_case			)
		# we force the name of each key to be the last file / folder name of the url path
		# if the url has arguments, we need to encode them with urllib.parse.quote_plus
		lowercase	    =       os.path.join(snake_case						,       urllib.parse.quote_plus(data_url.split('/'			)[-1]			)			)
		if os.path.exists(snake_case			) or not self.load_existing_dummy_data:
			return value
		else:
			# Backward compatibility, maybe deprecate at one point.
			# For many datasets with single url calls to dl_manager.download_and_extract,
			# the dummy_data.zip file is actually the zipped downloaded file
			# while now we expected the dummy_data.zip file to be a directory containing
			# the downloaded file.
			return path_to_dummy_data





	def       SCREAMING_SNAKE_CASE__		(			self			):
		pass





	def       SCREAMING_SNAKE_CASE__		(			self			):
		pass





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case			):
		def _iter_archive_members(snake_case			):
			# this preserves the order of the members inside the ZIP archive
			lowercase	    =       Path(self.dummy_file			).parent
			lowercase	    =       path.relative_to(snake_case			)
			with ZipFile(self.local_path_to_dummy_data			) as zip_file:
				lowercase	    =       zip_file.namelist()
			for member in members:
				if member.startswith(relative_path.as_posix()			):
					yield dummy_parent_path.joinpath(snake_case			)

		lowercase	    =       Path(snake_case			)
		lowercase	    =       _iter_archive_members(snake_case			) if self.use_local_dummy_data else path.rglob('*'			)
		for file_path in file_paths:
			if file_path.is_file() and not file_path.name.startswith(('.', '__')			):
				yield file_path.relative_to(snake_case			).as_posix(), file_path.open('rb'			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case			):
		if not isinstance(snake_case						,       snake_case			):
			lowercase	    =       [paths]
		for path in paths:
			if os.path.isfile(snake_case			):
				if os.path.basename(snake_case			).startswith(('.', '__')			):
					return
				yield path
			else:
				for dirpath, dirnames, filenames in os.walk(snake_case			):
					if os.path.basename(snake_case			).startswith(('.', '__')			):
						continue
					dirnames.sort()
					for filename in sorted(snake_case			):
						if filename.startswith(('.', '__')			):
							continue
						yield os.path.join(snake_case						,       snake_case			)



"""simple docstring"""


import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


SCREAMING_SNAKE_CASE			  =  logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				Tuple:







       """simple docstring"""
       if "token" in model_name_or_path:
              return "rag_token"
       if "sequence" in model_name_or_path:
              return "rag_sequence"
       if "bart" in model_name_or_path:
              return "bart"
       return None



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				List[str]:







       """simple docstring"""
       return max(metric_fn(UpperCAmelCase_		,   UpperCAmelCase_					) for gt in ground_truths					)



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				List[Any]:







       """simple docstring"""
       UpperCamelCase       			=		[line.strip() for line in open(UpperCAmelCase_		,   "r"					).readlines()]
       UpperCamelCase       			=		[]

       if args.gold_data_mode == "qa":
              UpperCamelCase       			=		pd.read_csv(UpperCAmelCase_		,   sep="\t"		,   header=UpperCAmelCase_					)
              for answer_list in data[1]:
                     UpperCamelCase       			=		ast.literal_eval(UpperCAmelCase_					)
                     answers.append(UpperCAmelCase_					)
       else:
              UpperCamelCase       			=		[line.strip() for line in open(UpperCAmelCase_		,   "r"					).readlines()]
              UpperCamelCase       			=		[[reference] for reference in references]

       UpperCamelCase       			=		UpperCamelCase       			=		UpperCamelCase       			=		0
       for prediction, ground_truths in zip(UpperCAmelCase_		,   UpperCAmelCase_					):
              total += 1
              em += metric_max_over_ground_truths(UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)
              fa += metric_max_over_ground_truths(UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)

       UpperCamelCase       			=		100.0 * em / total
       UpperCamelCase       			=		100.0 * fa / total

       logger.info(F"F1: {fa:.2f}"					)
       logger.info(F"EM: {em:.2f}"					)



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				List[Any]:







       """simple docstring"""
       UpperCamelCase       			=		args.k
       UpperCamelCase       			=		[line.strip() for line in open(UpperCAmelCase_		,   "r"					).readlines()]
       UpperCamelCase       			=		[line.strip() for line in open(UpperCAmelCase_		,   "r"					).readlines()]

       UpperCamelCase       			=		UpperCamelCase       			=		0
       for hypo, reference in zip(UpperCAmelCase_		,   UpperCAmelCase_					):
              UpperCamelCase       			=		set(hypo.split("\t"					)[:k]					)
              UpperCamelCase       			=		set(reference.split("\t"					)					)
              total += 1
              em += len(hypo_provenance & ref_provenance					) / k

       UpperCamelCase       			=		100.0 * em / total
       logger.info(F"Precision@{k}: {em: .2f}"					)



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				Optional[int]:







       """simple docstring"""
       def strip_title(UpperCAmelCase_					):
              if title.startswith("\""					):
                     UpperCamelCase       			=		title[1:]
              if title.endswith("\""					):
                     UpperCamelCase       			=		title[:-1]
              return title

       UpperCamelCase       			=		rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
           UpperCAmelCase_		,   return_tensors="pt"		,   padding=UpperCAmelCase_		,   truncation=UpperCAmelCase_		,   )["input_ids"].to(args.device					)

       UpperCamelCase       			=		rag_model.rag.question_encoder(UpperCAmelCase_					)
       UpperCamelCase       			=		question_enc_outputs[0]

       UpperCamelCase       			=		rag_model.retriever(
           UpperCAmelCase_		,   question_enc_pool_output.cpu().detach().to(torch.floataa					).numpy()		,   prefix=rag_model.rag.generator.config.prefix		,   n_docs=rag_model.config.n_docs		,   return_tensors="pt"		,   )
       UpperCamelCase       			=		rag_model.retriever.index.get_doc_dicts(result.doc_ids					)
       UpperCamelCase       			=		[]
       for docs in all_docs:
              UpperCamelCase       			=		[strip_title(UpperCAmelCase_					) for title in docs["title"]]
              provenance_strings.append("\t".join(UpperCAmelCase_					)					)
       return provenance_strings



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				Optional[int]:







       """simple docstring"""
       with torch.no_grad():
              UpperCamelCase       			=		rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
                  UpperCAmelCase_		,   return_tensors="pt"		,   padding=UpperCAmelCase_		,   truncation=UpperCAmelCase_					)

              UpperCamelCase       			=		inputs_dict.input_ids.to(args.device					)
              UpperCamelCase       			=		inputs_dict.attention_mask.to(args.device					)
              UpperCamelCase       			=		rag_model.generate(  # rag_model overwrites generate
                  UpperCAmelCase_		,   attention_mask=UpperCAmelCase_		,   num_beams=args.num_beams		,   min_length=args.min_length		,   max_length=args.max_length		,   early_stopping=UpperCAmelCase_		,   num_return_sequences=1		,   bad_words_ids=[[0, 0]]		,   )
              UpperCamelCase       			=		rag_model.retriever.generator_tokenizer.batch_decode(UpperCAmelCase_		,   skip_special_tokens=UpperCAmelCase_					)

              if args.print_predictions:
                     for q, a in zip(UpperCAmelCase_		,   UpperCAmelCase_					):
                            logger.info("Q: {} - A: {}".format(UpperCAmelCase_		,   UpperCAmelCase_					)					)

              return answers



def 						lowerCamelCase__      (				)->				Any:







       """simple docstring"""
       UpperCamelCase       			=		argparse.ArgumentParser()
       parser.add_argument(
           "--model_type"		,   choices=["rag_sequence", "rag_token", "bart"]		,   type=UpperCAmelCase_		,   help=(
               "RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the"
               " model_name_or_path"
           )		,   )
       parser.add_argument(
           "--index_name"		,   default=UpperCAmelCase_		,   choices=["exact", "compressed", "legacy"]		,   type=UpperCAmelCase_		,   help="RAG model retriever type"		,   )
       parser.add_argument(
           "--index_path"		,   default=UpperCAmelCase_		,   type=UpperCAmelCase_		,   help="Path to the retrieval index"		,   )
       parser.add_argument("--n_docs"		,   default=5		,   type=UpperCAmelCase_		,   help="Number of retrieved docs"					)
       parser.add_argument(
           "--model_name_or_path"		,   default=UpperCAmelCase_		,   type=UpperCAmelCase_		,   required=UpperCAmelCase_		,   help="Path to pretrained checkpoints or model identifier from huggingface.co/models"		,   )
       parser.add_argument(
           "--eval_mode"		,   choices=["e2e", "retrieval"]		,   default="e2e"		,   type=UpperCAmelCase_		,   help=(
               "Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"
               " precision@k."
           )		,   )
       parser.add_argument("--k"		,   default=1		,   type=UpperCAmelCase_		,   help="k for the precision@k calculation"					)
       parser.add_argument(
           "--evaluation_set"		,   default=UpperCAmelCase_		,   type=UpperCAmelCase_		,   required=UpperCAmelCase_		,   help="Path to a file containing evaluation samples"		,   )
       parser.add_argument(
           "--gold_data_path"		,   default=UpperCAmelCase_		,   type=UpperCAmelCase_		,   required=UpperCAmelCase_		,   help="Path to a tab-separated file with gold samples"		,   )
       parser.add_argument(
           "--gold_data_mode"		,   default="qa"		,   type=UpperCAmelCase_		,   choices=["qa", "ans"]		,   help=(
               "Format of the gold data file"
               "qa - a single line in the following format: question [tab] answer_list"
               "ans - a single line of the gold file contains the expected answer string"
           )		,   )
       parser.add_argument(
           "--predictions_path"		,   type=UpperCAmelCase_		,   default="predictions.txt"		,   help="Name of the predictions file, to be stored in the checkpoints directory"		,   )
       parser.add_argument(
           "--eval_all_checkpoints"		,   action="store_true"		,   help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number"		,   )
       parser.add_argument(
           "--eval_batch_size"		,   default=8		,   type=UpperCAmelCase_		,   help="Batch size per GPU/CPU for evaluation."		,   )
       parser.add_argument(
           "--recalculate"		,   help="Recalculate predictions even if the prediction file exists"		,   action="store_true"		,   )
       parser.add_argument(
           "--num_beams"		,   default=4		,   type=UpperCAmelCase_		,   help="Number of beams to be used when generating answers"		,   )
       parser.add_argument("--min_length"		,   default=1		,   type=UpperCAmelCase_		,   help="Min length of the generated answers"					)
       parser.add_argument("--max_length"		,   default=50		,   type=UpperCAmelCase_		,   help="Max length of the generated answers"					)

       parser.add_argument(
           "--print_predictions"		,   action="store_true"		,   help="If True, prints predictions while evaluating."		,   )
       parser.add_argument(
           "--print_docs"		,   action="store_true"		,   help="If True, prints docs retried while generating."		,   )
       UpperCamelCase       			=		parser.parse_args()
       UpperCamelCase       			=		torch.device("cuda" if torch.cuda.is_available() else "cpu"					)
       return args



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				List[Any]:







       """simple docstring"""
       UpperCamelCase       			=		{}
       if args.model_type is None:
              UpperCamelCase       			=		infer_model_type(args.model_name_or_path					)
              assert args.model_type is not None
       if args.model_type.startswith("rag"					):
              UpperCamelCase       			=		RagTokenForGeneration if args.model_type == "rag_token" else RagSequenceForGeneration
              UpperCamelCase       			=		args.n_docs
              if args.index_name is not None:
                     UpperCamelCase       			=		args.index_name
              if args.index_path is not None:
                     UpperCamelCase       			=		args.index_path
       else:
              UpperCamelCase       			=		BartForConditionalGeneration

       UpperCamelCase       			=		(
           [f.path for f in os.scandir(args.model_name_or_path					) if f.is_dir()]
           if args.eval_all_checkpoints
           else [args.model_name_or_path]
       )

       logger.info("Evaluate the following checkpoints: %s"		,   UpperCAmelCase_					)

       UpperCamelCase       			=		get_scores if args.eval_mode == "e2e" else get_precision_at_k
       UpperCamelCase       			=		evaluate_batch_eae if args.eval_mode == "e2e" else evaluate_batch_retrieval

       for checkpoint in checkpoints:
              if os.path.exists(args.predictions_path					) and (not args.recalculate):
                     logger.info("Calculating metrics based on an existing predictions file: {}".format(args.predictions_path					)					)
                     score_fn(UpperCAmelCase_		,   args.predictions_path		,   args.gold_data_path					)
                     continue

              logger.info("***** Running evaluation for {} *****".format(UpperCAmelCase_					)					)
              logger.info("  Batch size = %d"		,   args.eval_batch_size					)
              logger.info("  Predictions will be stored under {}".format(args.predictions_path					)					)

              if args.model_type.startswith("rag"					):
                     UpperCamelCase       			=		RagRetriever.from_pretrained(UpperCAmelCase_		,   **UpperCAmelCase_					)
                     UpperCamelCase       			=		model_class.from_pretrained(UpperCAmelCase_		,   retriever=UpperCAmelCase_		,   **UpperCAmelCase_					)
                     model.retriever.init_retrieval()
              else:
                     UpperCamelCase       			=		model_class.from_pretrained(UpperCAmelCase_		,   **UpperCAmelCase_					)
              model.to(args.device					)

              with open(args.evaluation_set		,   "r"					) as eval_file, open(args.predictions_path		,   "w"					) as preds_file:
                     UpperCamelCase       			=		[]
                     for line in tqdm(UpperCAmelCase_					):
                            questions.append(line.strip()					)
                            if len(UpperCAmelCase_					) == args.eval_batch_size:
                                   UpperCamelCase       			=		evaluate_batch_fn(UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)
                                   preds_file.write("\n".join(UpperCAmelCase_					) + "\n"					)
                                   preds_file.flush()
                                   UpperCamelCase       			=		[]
                     if len(UpperCAmelCase_					) > 0:
                            UpperCamelCase       			=		evaluate_batch_fn(UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)
                            preds_file.write("\n".join(UpperCAmelCase_					)					)
                            preds_file.flush()

                     score_fn(UpperCAmelCase_		,   args.predictions_path		,   args.gold_data_path					)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE			  =  get_args()
      main(args)





